The paper presents the first stage of a study on characterization of species using very short DNA fragments from COI gene (barcode gene). Gene fragments, not complete sequence, are a common scenario working with degraded samples or in pres- ence of noise. The proposed technique is based on a novel prototype-based classification approach and a modified General Regression Neural Network (GRNN). The proposed system can use 200 bp (over 650 bp of the COI gene) even if constituted by blocks of 50 bp scattered in random positions of the gene sequence.
The General Regression Neural Network to Classify Barcode and mini-barcode DNA
Riccardo Rizzo;Antonino Fiannaca;Massimo La Rosa;Alfonso Urso
2014
Abstract
The paper presents the first stage of a study on characterization of species using very short DNA fragments from COI gene (barcode gene). Gene fragments, not complete sequence, are a common scenario working with degraded samples or in pres- ence of noise. The proposed technique is based on a novel prototype-based classification approach and a modified General Regression Neural Network (GRNN). The proposed system can use 200 bp (over 650 bp of the COI gene) even if constituted by blocks of 50 bp scattered in random positions of the gene sequence.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
